Who are you calling food insecure?

Every year, the USDA Economic Research Service (ERS) reports rates of food security in the United States. In 2018, 11.1% of U.S. households were estimated to be food insecure, down from a recent-history high of 14.9% in 2011.

These official statistics on food security are often interpreted in the media and by lay audiences as a measure of hunger. But, that’s not exactly what the USDA-ERS measures. A new paper by Sunjin Ahn, Travis Smith, and Bailey Norwood in Applied Economics Perspectives and Policy does a great job de-mystifying how official government measures of food insecurity are actually calculated. They also ably explain and articulate what other survey researchers must do to produce results that approximate the official measures.

Food insecurity is measured by the US Census Bureau asking a large sample of nationally-representative U.S. households a series of 10 questions (plus an additional 8 questions if there are children in the household) like how often, “In the last 12 months, were you ever hungry, but didn't eat, because you couldn't afford enough food?” or how often “I couldn’t afford to eat balanced meals.” A score is then calculated based on the frequency with which people respond affirmatively to the questions. If the score is high enough, the household is deemed food insecure. Seen in this way, food insecurity is probably best interpreted as a measure of a household’s perception of food affordability, although it almost surely positively correlated with hunger. The ERS has more information on how food security differs from hunger, and on the details of their measurement of food security here.

Ahn, Smith, and Norwood point out another issue that is not widely appreciated. They write:

“To avoid overburdening respondents with unnecessary questions in the CPS‐FSS [Census Bureau Current Population Survey - Food Security Supplement] survey, surveyors first conduct a screening process. If a household’s income is greater than 185% of the poverty threshold, and they answer

(1) “no” to “… did you ever run short of money and try to make your food or your money go further,” or

(2) “enough of the kinds of food (I/we) want to eat” from the question “Which of these statements best describes the food eaten in your household …,”

they are assumed to be food secure and are not administered the Food Security questionnaire (ERS 2015b). This screening process varies: In a 2012 design description, the first of the above questions was not used (ERS 2012a), and documentation of the survey suggests sometimes the income threshold is 200% of the poverty threshold. Though it is recognized that some of the individuals screened out of the questions will in fact be food insecure, the screening was still seen as desirable because it reduces respondent burden (ERS 2015a). Thus, the CPS‐FSS food insecurity rates are a function of responses to food insecurity questions conditional on the statistical screening procedures employed.”

Ahn, Smith, and Norwood’s paper is mainly framed around the question of whether opt-in, internet-based surveys can mimic the official government estimates of food insecurity. However, their results make abundantly clear the critical role of the income threshold in setting official food insecurity rates. In short, if we simply counted the scores on the food insecurity questions and ignored income, we would find MUCH higher rates of measured food insecurity. Before applying the income-cutoff, Ahn, Smith, and Norwood find food insecurity rates of 43% (in a 2016 survey) and 31% (in a 2017 survey). After applying the income cut-offs (essentially assuming anyone with an income over 180% of the poverty line can’t be food insecure) and some demographic weighting, the authors find opt-in internet surveys can produce estimates of food insecurity that are similar to that reported by the USDA-ERS.

I’m a little unsure of how to interpret these findings. On the one hand, I’m left with a sense that the official food insecurity statistics are heavily influenced by a somewhat arbitrary income cut-off, and that perhaps the official measure of food insecurity are too imprecise at measuring the construct we are really after. Another, reasonable, albeit alarming, conclusion is that there may a lot more food insecure people than we thought.

Consumer Demand for Redundant Food Labels

That’s the title of a new working paper co-authored with Lacey Wilson. Here is the abstract:

“Previous studies, as well as market sales data, show some consumers are willing to pay a premium for redundant or superfluous food labels that carry no additional information for the informed consumer. Some advocacy groups have argued that the use of such redundant labels is misleading or unethical. To determine whether premiums for redundant labels stem from misunderstanding or other factors, this study seeks to determine whether greater knowledge of the claims - in the form of expertise in food production and scientific literacy - decreases willingness to pay for redundant labels. We also explore whether de-biasing information influences consumers’ valuations of redundant labels. An online survey of 1,122 U.S. consumers elicited preferences for three redundantly labeled products: non-GMO sea salt, gluten-free orange juice, and no-hormone-added chicken breast. Respondents with farm experience report lower premiums for non-GMO salt and no-hormone-added chicken. Those with higher scientific literacy state lower premiums for gluten-free orange juice. However, after providing information about the redundancy of the claims, less than half of respondents who were initially willing to pay extra for the label are convinced otherwise. Over 30% of respondents counter-intuitively increase their premiums, behavior that is associated with less a priori scientific knowledge. The likelihood of “overpricing” redundant labels is associated with willingness-to-pay premiums for organic food, suggesting at least some of the premium for organic is a result of misinformation.”

The figure below shows a key result. People place a $0 premium on non-GMO salt, gluten-free orange juice, and hormone-free chicken have significantly higher scientific literacy scores than people who place positive or negative premiums on these redundantly labeled products.

Measuring changes in supply versus changes in demand

I just finished up a new working paper with Glynn Tonsor that shows how to determine the extent to which a change in price (or quantity) results from a change in supply and/or demand. For some time, Glynn has been reporting updated retail demand indices for meat products. In this new paper, we show how to calculate an analogous supply index, which might provide a useful way to determine how much productivity is changing over time. The basic idea is that we want a way to separate changes in quantity demanded (or supplied) versus a shift in the demand curve (or the supply curve). We also show how the two indicies can be used to determine changes in consumer and producer economic well-being over time.

Here’s the motivation:

“In 2015, per-capita beef consumption in the U.S. reached a record low of 54 lbs/person, falling almost 20% over the prior decade from 2005 to 2015 alone (USDA, Economic Research Service, 2020). Why? Some environmental, public health, and animal advocacy organizations heralded the decline as an indicator of their efforts to convince consumers to reduce their demand for beef; others argued, instead, the change was a result of supply-side factors such as drought and higher feed prices (e.g., Strom, 2017). Per-capita beef consumption subsequently rebounded, and in 2018 was almost 6% higher than in 2015. Dramatic fluctuations in corn, soybean, and wheat prices in the late 2000s through the mid-2010s led to similar heated debates about whether and to what extent price rises were due to demand (e.g., biofuel policy and rising incomes in China) or supply (e.g., drought in various regions of the world) factors (e.g., Abbot, Hurt, Tyner 2019; Carter, Rouser, and Smith, 2016; Hochman, Rajagopal, and Zilberman, 2010; Roberts and Schlenker, 2013). These cases highlight the challenge of interpreting market dynamics and the need for metrics that can decompose price or quantity changes to reveal underlying drivers and consequences.”

We calculate the supply and demand indicies for a number of agricultural markets and time periods. First, consider changes in supply and demand in the fed cattle market since the 1950s, as shown in the figure below. The demand index trended positively from 1950 through the mid 1970s. The demand index peaked at a value of 204 in 1976, and it hasn’t been as high since. Demand fell through the 1980s and early 1990s before rebounding. Since 2010, the demand index has been at values just below the 1970’s peak. The supply index trend was positive from 1950 up till about 2000, but has been stagnant except for the past couple decades. Nonetheless, the 2018 supply index value is the highest of the entire time period since 1950. The figure shows a significant drop in the supply index that began in 2013 and bottomed out in 2015, which is likely a result of drought in the great plains and from high feed prices. The fact that the supply index dropped during this period while the demand index remained relatively flat helps provide insight into the debate discussed in the quote above.

One can also calculate changes in producer and consumer surplus over time. The following figure calculates the year-to-year changes. On average, from 1980 to 2018, producer surplus increased $2.7 billion each year and consumer surplus increased $0.58 billion each year. Despite these averages, there is a high degree of year-to-year variability. The largest annual change in producer surplus was $34 billion from 2015 to 2016; the largest decline in producer surplus was -$28 billion from 2013 to 2014.

Here’s how changes in the supply index compare for the three main meat categories. Chicken supply shifts have far outpaced that for hogs or cattle. The 2018 chicken supply index value is 380, meaning chicken supply is (380-100) = 280% higher than in 1980. By contrast, hog and beef supply are only 66% and 28% higher, respectively, than in 1980. These differences are likely explained by differential productivity patterns in these sectors. The rise in hog productivity since 2000 corresponds with a time period over which the industry became increasingly vertically integrated, increasingly mirroring the broiler chicken sector. The much longer biological production lags in beef cattle (which range from two to three years from the time a breeding decision is made until harvest) and less integrated nature of the beef cattle industry help explain the smaller increases in the supply index in this sector as compared to pork and chicken. We also show, in the paper, that these supply indicies correlate in intuitive ways with changes in factors like feed prices, drought, and aggregate U.S. agricultural total factor productivity.

One of the useful aspects of the supply and demand indicies is that they can be applied for highly disaggregated geographic units. To illustrate, we calculated U.S. county-level supply indicies. Here are the changes in U.S. supply indicies in the past couple years relative to 2000. Perhaps surprisingly, many areas of Ohio, Indiana, and southern Illinois have experienced negative corn supply shocks in 2016-2018 relative to 2000. The expanded geographical area of U.S. corn production (e.g more acres in the Dakotas) over this period helped mitigate national corn market effects of the adverse Eastern Cornbelt supply shocks. Note that corn yields and total production have increased significantly in many of the red counties over time, and this illustrates the importance of calculating a supply index rather than just looking at yield or production. The supply index gives us a feel for how much more (or less) is produced in 2018 relative to what we would have expected if the level of technology, weather, etc. were the same as in the year 2000.

There is a lot more in the paper.

Malthusian Inversion

I’ve noticed several articles in the past few weeks talking about slowing or even falling population growth. This article in the Economist discussed the fact that South Korea’s fertility rate is now below replacement level (i.e., fewer babies are being born than there are parents), and their figure shows the strong negative correlation between income (or GDP) of a country and a country’s fertility rate. The richer we get, it seems the fewer babies we want or need. Then, came this opinion article in the New York Times a couple days ago about the “Chinese Population Crisis,” in which Ross Douthat argues, “Unlike most developed countries, China is growing old without first having grown rich.”

We’ve all probably been adequately exposed to the concerns and problems associated with over-population from the writings of Malthus to Ehrlich’s Population Bomb. Less well appreciated are the benefits and costs associated with a falling population. For one take on the potential concerns, see this 2013 piece by Kevin Kelly entitled the “Underpopulation Bomb.” He states the problem as follows:

“This is a world where every year there is a smaller audience than the year before, a smaller market for your goods or services, fewer workers to choose from, and a ballooning elder population that must be cared for. We’ve never seen this in modern times; our progress has always paralleled rising populations, bigger audiences, larger markets and bigger pools of workers. It’s hard to see how a declining yet aging population functions as an engine for increasing the standard of living every year.”

A smaller population would no doubt produce some benefits. Probably the most obvious benefit is that a smaller population would lessen human’s demands on our environment and natural resources. There is already evidence of this de-materialization. Check out Andrew McAfee’s book More from Less, where he argues that technology has led us past peak demand for many natural resources.

Among the adverse consequences, however, of falling population is likely to be downward pressure on farm incomes. The Malthusian concern implied a large population that was incapable of sufficiently feeding itself. For humanity writ large, this outcome would have been a tragedy. Farmers (or at least land owners), however, would have likely benefited from this dire outcome. More people demanding more food would have driven up food prices, land prices, and farm income. Innovation and productivity growth, fortunately, prevented the hunger problems that would have accompanied a rising population.

One crude way to see whether population growth is out-pacing the effect of innovation is to look at the long-term trend in food and agricultural prices. Here is a graph I created based on USDA data on three major commodity crop prices over time. The long-term trend is negative, suggesting innovation has outpaced the effects of population and income growth.

Lower prices seems bad for farmers, but if they are able to sell more output using fewer resources, they also benefit (see this paper by Alston for some discussion on the farmer benefits and costs from innovation). Still, it is probably safe to say that farmers and the agricultural sector have largely come to expect a rising world population to support demand growth and offset some of the downward pressure on prices. Population projections suggest that expectation may not hold out into the future.

Indeed, research by my Purdue colleagues Uris Baldos and Tom Hertel suggests the effects of population on crop prices is likely to be much lower than what we’ve experienced in the past. They consider the effect of three factors on crop prices and production: population, income, and innovation. From 1960 to 2006, their findings indicate that the effect of innovation pushed down crop prices more than rising income and population pushed up prices, leading to an overall fall in prices, consistent with the graph above. What do they predict for the future based on expected trends in innovation, population, and income? Falling prices. They predict that, going out to 2050, the price increasing effects of population will be about half what they were from 1960 to 2006. Thus, rather than the Malthusian population bomb, we seem to be heading to a sort of Malthusian inversion.

It’s also important to note that population growth will be unevenly distributed across the world. Data and projections from the United Nations show very different anticipated population trends in low-, middle-, and high-income countries. Here is a figure I created based on their data. In 1950, population was 45%, 70%, and 82% lower than it is today in high-, middle-, and low-income countries. By 2010, the UN is projecting population will be 3%, 23%, and 220% higher in high-, middle-, and low-income countries.

The implications for U.S. farmers are many fold. For one, demand growth (at least from population growth) is likely to occur outside this country, highlighting the importance of trade. Within the U.S., rising income, and demand for quality, may play an increasingly important role in supporting farm incomes in the years to come. Finally, flat or declining population, along with innovation, have the potential to have positive environmental outcomes, and it will important to think about appropriate farm policy in light of these trends.

Migration, Agriculture, and Local Food

I recently listened to an episode of a Radiolab podcast entitled There and Back Again. The episode is about the history of science related to the migratory patterns of birds and other animals. It seems our forebearers had some far-fetched answers to the question “where do the animals go in the winter?” Some folks apparently thought birds transformed into other species or even flew to the moon to escape winter. The episode also delves into the modern day science of tracking animal migratory patterns using sensors and satellites. It’s amazing how many thousands of miles animals, birds, fish, and insects travel on an annual basis; there are even some birds that fly annually from the north to south pole.

All this got me thinking about our modern-day discussions about food and agriculture. A popular notion today is that we should eat what is local and eat what is in season, and there is a notion that eating in this manner is more natural, perhaps closer to the “good old days” when our ancestors weren’t plagued by the modern conveniences of hyper processed food.

It is interesting to contrast this local, seasonal view of what is presumably natural for humans, with what is actually quite natural for birds and many other animals who expend great effort to avoid eating seasonally. In our modern world, we have figured out how to specialize food production in areas with comparative advantages (e.g., veggies in California, citrus in Florida, cherries in Michigan, corn and soy in Indiana) and then move the food to the people by boat, rail, and truck. That is, we’ve learned to migrate food to people rather than the old “natural” way of migrating people to the food. Indeed, before humans discovered agriculture, we spent our time following our food while it migrated across the landscape. For example, some native American tribes seasonally followed the bison across the Great Plains. Eating locally and seasonally is decidedly not natural.

Now, I’ve argued that whether something is “natural” has no moral bearing per se, but this episode helps draw out some of the apparent contradictions in our beliefs about naturalness and historical reality. By the way, I’m looking forward to reading Alan Levinovitz’s forthcoming book entitled Natural: How Faith in Nature’s Goodness Leads to Harmful Fads, Unjust Laws, and Flawed Science.